COVID-19 and Mobility Data Analysis with Poisson Mixed Effects Regression Models to Account for Spatial and Temporal Correlations
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 is a worldwide pandemic. We conducted a study to estimate the relationship and time lag between population mobility and COVID-19 infection curves based on weekly and census tractlevel aggregated data (i.e. BlueDot mobility data, iPHIS surveillance data, and census data) in Greater Toronto Area (2020.03.15-2021.02.07). COVID-19 weekly confirmed new cases (i.e. weekly cases) was the outcome and proportion of time outside the home (i.e. mobility) was the primary predictor. We used the Poisson mixed effects models with a first-order autoregressive ( AR(1)) structure to estimate mobility effect and applied a modified K-fold cross validation to find an optimal time lag. The results show that the optimal time lag is around six week. Mobility has significant and negative effects on COVID-19 weekly cases during an epidemic wave. During a flat period, the mobility effect is not strong.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".